Recent efforts also focused on regression-based approaches that cope better with overlapped objects and crowded scenarios. For example, [1]} proposed SAU-Net, an extension of the U-Net segmentation network [2]} with a Self-Attention module for counting by density regression. In [3]}, another regression-based counting model is introduced, enhanced by regulating activation maps from the final convolution layer of the network with coarse ground-truth activation maps generated from simple dot annotations. More, in [4]}, the authors proposed a novel deep neural network architecture adapted from the Inception family [5]} of networks called Count-ception.
In [6]}, the so-called CSRNet [7]}, a regression-based CNN suitable for counting objects in several contexts (already employed in ch:uda-counting for traffic density estimation), is employed to estimate cell densities in immunohistochemically stained sections of breast tissue. Jiang and Yu proposed two different regression-based cell counting approaches [8]}, [9]}, again, based on the estimation of density maps.
Finally, authors in [10]} presented another regression model based on density estimation where auxiliary convolutional neural networks are employed to assist in the training of intermediate layers.
Other regression-based strategies have also been devised to deal with densely concentrated cells but still generating individual cell detections, such as [11]}, [12]}, [13]} and [14]}.
These approaches first generate intermediate maps that indicate the likelihood of each pixel being the center of a cell in the image, and then convert them into detections by applying some form of Non-Maximum Suppression (NMS).
